Document recommendation is an essential constituent in a user-oriented content management system. Accurate document recommendation may potentially enhance users' working efficiency and improve user experience, such as for web browsing, call center operations etc. The construction of user's hobbies or preferences is automated by integrating information obtained from the user's activities, such as browsing histories, call record history, etc. When a new user has insufficient historical data, the main challenge for such a recommendation system is how to conduct personalized recommendation for the new user, which is typically referred to as a cold start problem.
Traditional document recommendation approaches always depends on document contents, user information, such as explicit or implicit user feedbacks, user's profiles, and sometimes uses help of semantic analysis via a thesaurus. Another approach is collaborative filtering (CF), which is widely used for web recommendation. The CF approach can make recommendations by computing similarity or correlation between items from among user's activity logs. A key issue with the CF approach is on defining a correlation function (or a distance function) between users and documents. It becomes relatively cumbersome and difficult to accurately construct the correlation function when few data points are available. Thus, the above two approaches cannot effectively overcome the cold start problem.